The present disclosure relates to motion estimation and, more particularly, to structures and techniques for computing matching criteria typically employed in motion estimation.
Video coding employing Motion Estimation (ME) and/or Motion Compensation (MC) is widely used in various video coding standards and/or specifications, such as MPEG [see Moving Pictures Experts Group, ISO/IEC/SC29/WG11 standard committee]. Advances, for example, in integrated circuit technology, in recent times have made it possible to implement block matching techniques in hardware, such as with silicon or semiconductor devices. An excellent discussion of ME may be found in Bhaskara and Constantis, [see V. Bhaskaran and K. Konstantinides. “Image and Video Compression Standards: Algorithms and Architectures”, Kluwer Academic Publishers, 1995.]
FIG. 1 shows a block diagram of an embodiment of an MPEG type video encoder. For this particular embodiment, a process of block matching involves a reference block and a search window. There are many matching criteria developed in the literature for matching a block of pixels in a video frame (usually the current frame to be encoded) with a block of pixels in the search window in another frame (usually a previous frame). A “reference block” in this context refers to a selected group of pixels from the current frame to be encoded. In MPEG, this is popularly called a macroblock and usually the size of this macroblock is 16×16. A search window in this context refers to a region of pixels from another frame, frequently the previous frame, to be searched to determine the best match. The “Sum-of-Absolute-Difference” (SAD), generally equivalent to the “Mean Absolute Difference” (MAD), is popular amongst a variety of potential matching criteria because of its low computational burden with the ability to omit multiplication or division. Some other examples of matching criteria include Mean Absolute Difference (MAD), Mean Square Error (MSE), Normalized Cross-Correlation Function, Minimized Maximum Error (MiniMax), etc. Of course, any one of a variety of matching criteria may be employed in block matching and, in this context, no particular matching criteria is preferred over any other; although, depending on the particular application, there may be reasons to prefer one over another.
Usually, a search begins with the motion vector, MV=(0,0) or no motion. For this particular embodiment, a search window is the block of pixels from a previous frame around MV=(0,0). The block size and choice of search window size typically reflects an implementation trade-off; therefore, again, no particular size is necessarily preferred over another in this context. For example, the larger the search window, the higher the computational complexity and memory/data bandwidth capability desired, but, likewise, improved is the chance to get a good match. FIG. 1 shows reference block A in the current frame (I) and the best match block B within the search window in the previous frame (P). The displacement (dx, dy) of the matching block B at location/coordinate (x+dx, y+dy) from the reference block A at coordinate (x, y) is called the motion vector and represented as MV=(dx, dy). The technique to compute this MV is popularly referred to as Motion Estimation (ME). There are several motion estimation techniques in the literature [see, for example, V. Bhaskaran and K. Konstantinides. “Image and Video Compression Standards: Algorithms and Architectures”, Kluwer Academic Publishers, 1995.] In this particular embodiment, full-search (FS) Block Matching is employed. However, this approach may be demanding from the viewpoint of raw computational power as well as the appropriate data bandwidth rate desired to support such an approach.